AI agent frameworks: 12 options compared by 5 selection criteria

The article walks through how to evaluate and select an AI agent framework in five phases: understanding what frameworks do, identifying your decision criteria, comparing 12 major options, matching frameworks to five common business scenarios, and avoiding four typical implementation traps.

AI agent frameworks handle four core tasks every agent needs: state management (tracking which step the agent is on and what results it holds), tool execution (letting AI call internal APIs and databases), inter-agent coordination (when multiple AIs collaborate), and execution logging (a record of which tools the agent called and why). Building these yourself for every new use case wastes engineering effort; frameworks provide them as reusable primitives. However, building a simple agent that completes in one API call may be simpler to write without a framework—the trade-off is between framework learning cost and code maintenance cost as complexity grows.

Frameworks split into two types: code-based (Python- or TypeScript-written, precise control over branching and permissions, but require engineering effort) and no-code tools (screen-based, can be built by business staff, but limited to provided features). A practical path is to start with no-code for small wins, then move to code-based frameworks once the organization is ready.

The five decision axes are: (1) language match—does your team know Python, TypeScript, or .NET?; (2) multi-agent and control flexibility—do you need to pause and branch based on human approval, or run parallel role-based agents?; (3) internal data and RAG—do you need to search company documents, and how critical is search quality?; (4) MCP and tool breadth—how many external systems will you integrate, and is MCP support important?; (5) development status and security—is the framework actively maintained, and does its license fit your use case?. Using these five filters cuts the 12 candidates down to a short list fast.

The 12 major frameworks break into three groups. Code-based (8): LangGraph (about 3.9k GitHub stars) excels at precise branching and state control, letting you pause execution, check state, rewrite it, and resume—ideal for approval workflows; Microsoft Agent Framework (released April 3, 2026, v1.0) unifies Semantic Kernel and AutoGen for enterprise use, supports both Python and .NET, and lets teams migrate existing assets; CrewAI (about 5.7k stars) specializes in role-based multi-agent teams that can be built in short cycles; OpenAI Agents SDK is lightweight and supports 100+ LLMs, ideal for quick proof-of-concept; Claude Agent SDK (Anthropic) has built-in file and command-line tools, strong for code automation; Google ADK (about 2.1k stars) is Apache 2.0 open-source and pairs well with Google Cloud; Mastra (about 2.7k stars) is TypeScript-only, unifies 40+ model providers, and lets Web teams avoid a language switch; PydanticAI (about 1.9k stars) uses Pydantic type definitions to guarantee output structure and retry on validation failure.

RAG-specialist (2): LlamaIndex (about 5.1k stars) ingests and indexes company documents for fast search foundation building; Haystack (about 2.6k stars) excels at large-scale document processing and lets you tune search accuracy per pipeline stage.

No-code (2): Dify (about 15.1k stars) uses screen-based workflow composition, supports PDFs and PowerPoint, offers 50+ built-in tools, has Japanese documentation, and runs via Docker Compose (Apache 2.0 base license plus custom terms—confirm commercial use first); n8n (about 19.9k stars) connects 1500+ external services and scales from prototype to production in one environment (Sustainable Use License plus commercial license—check scope first).

Five scenarios show how to pick: (1) Approval-flow work (e.g., expense approval above a threshold)—use LangGraph to pause, check, edit state, resume, and auto-recover from failure; (2) parallel multi-task work (e.g., competitor research split across info gathering, summarization, and report generation)—use CrewAI with roles and Flows for event-driven control; (3) non-engineer teams—use Dify to let business staff build workflows and RAG with no coding; (4) TypeScript Web teams—use Mastra to keep one language and inherit your CI/CD; (5) enterprise rollout—use Microsoft Agent Framework v1.0 to leverage existing Semantic Kernel and AutoGen investments, support Python and .NET, and inherit Microsoft's governance and audit hooks.

Four common failure modes are: choosing based on feature count alone (leading to six-month production failures); prioritizing proof-of-concept over production-ready design (approval, logging, error recovery); launching without logging and evaluation infrastructure (making it impossible to improve); and underestimating switching costs and vendor lock-in when a framework stalls (AutoGen's October 2025 transition to maintenance-only is a cautionary tale). The 2026 landscape shows MCP becoming the de facto tool-integration standard, TypeScript frameworks growing rapidly, and Microsoft formally establishing Enterprise support with Agent Framework v1.0. Framework choice now often hinges on visualization and evaluation infrastructure readiness—a framework's features matter less if you cannot observe and measure its behavior in production.